Cardiovascular (CV) disease affects more than a third of adults in the United States and is associated with significant morbidity and mortality. Improvements in symptom recognition, treatment, health behavior modification, and medication adherence could reduce the burden of CV disease. In the current digital age, needed is a better understanding of how information on social media sites may inform our approaches to improving CV health through novel methodologies. We propose to study the conversation on Twitter about several CV diseases (hypertension [HTN], diabetes [DM], congestive heart failure [CHF], myocardial infarction [MI], sudden cardiac arrest [SCA]) and their associated sequelae (e.g. symptoms, risk factors, health behaviors, medication adherence, outcomes). First, in Aim 1 we will characterize tweets related to CV diseases and associated sequelae by frequency, relevance, content, accuracy, source, temporal characteristics, geography, and demographics. In Aim 2, we will then use these data to measure the extent to which CV diseases and health behaviors reported via Twitter correlate with the known epidemiology of these conditions. Aim 3 will explore temporal trends and news shocks to reveal the evolution, diffusion, and transformation of CV health data with a goal of creating a forecasting model to determine when and how to optimally disseminate CV high impact health messages. Finally, in Aim 4 we will identify and validate a sample of tweeters with self-reported CV diseases. We will then use Twitter to deliver high impact CV disease-specific information to improve patient activation and disease management. The long term goal of this proposal is to better understand the uses and limitations of studying this social and digital form of communication as an approach to improving CV health and health behaviors.